Research map / Graph learning / Graph self-supervised learning
Independence Promoted Graph Disentangled Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add HSIC independence regularization to disentangled graph representations | DisenGCN-style node classification · Cora | ACC: 83.7 -> 84.1 | Helped |
| Add HSIC independence regularization to disentangled graph representations | DisenGCN-style node classification · Citeseer | ACC: 73.2 -> 74.0 | Helped |
| Add HSIC independence regularization to disentangled graph representations | DisenGCN-style node classification · Pubmed | ACC: 80.5 -> 81.2 | Helped |
| Add HSIC independence regularization to disentangled graph representations | DisenGCN-style node classification · Cora | F1: 83.6 -> 84.2 | Helped |
| Add HSIC independence regularization to disentangled graph representations | DisenGCN-style node classification · Citeseer | F1: 72.0 -> 72.8 | Helped |
| Add HSIC independence regularization to disentangled graph representations | DisenGCN-style node classification · Pubmed | F1: 80.9 -> 81.7 | Helped |
| Add HSIC independence regularization to disentangled graph representations | DisenGCN-style node clustering · Cora | ACC: 75.5 -> 76.1 | Helped |
| Add HSIC independence regularization to disentangled graph representations | DisenGCN-style node clustering · Citeseer | ACC: 68.2 -> 68.9 | Helped |
| Add HSIC independence regularization to disentangled graph representations | DisenGCN-style node clustering · Pubmed | ACC: 77.0 -> 77.8 | Helped |
| Add HSIC independence regularization to disentangled graph representations | DisenGCN-style node clustering · Cora | F1: 24.8 -> 25.3 | Helped |
| Add HSIC independence regularization to disentangled graph representations | DisenGCN-style node clustering · Citeseer | F1: 22.6 -> 23.2 | Helped |
| Add HSIC independence regularization to disentangled graph representations | DisenGCN-style node clustering · Pubmed | F1: 31.2 -> 32.1 | Helped |
Closest papers by meaning
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- Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models (NeurIPS 2024 · similarity 0.88)
- Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks (AAAI 2025 · similarity 0.87)
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- DUPLEX: Dual GAT for Complex Embedding of Directed Graphs (ICML 2024 · similarity 0.86)
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